title: Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.
abstract: Smartphone-based activity recognition (SP-AR) recognizes users' activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.
authors.affiliation: Division of Information and Computer Engineering, Ajou University, San 5 Woncheon-dong, Suwon 443-749, Korea. leesw@ajou.ac.kr.
authors.forename: Adil Mehmood
authors.initials: AM
authors.lastname: Khan
authors.forename: Muhammad Hameed
authors.initials: MH
authors.lastname: Siddiqi
authors.forename: Seok-Won
authors.initials: SW
authors.lastname: Lee
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods; D056044:Actigraphy / Q000295:instrumentation* / Q000379:methods; D000465:Algorithms; D040421:Cell Phone; D003199:Computer Systems; D034201:Computers, Handheld; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D008904:Miniaturization; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014159:Transducers